Digital content and entertainment

Imdb sentiment analysis

Importing and analysing the dataset

from keras.datasets import imdb
import nltk
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.utils import pad_sequences
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Embedding, Flatten
from keras.callbacks import EarlyStopping
import string

(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words = 10000, index_from = 3) #select top 10000 frequent words

print(f"Length of X_train {len(X_train)}")
print(f"Length of X_test {len(X_test)}")

Length of X_train 25000
Length of X_test 25000

Sequence adding

max_review_length = 500
X_train = pad_sequences(X_train, maxlen = max_review_length)
X_test = pad_sequences(X_test, maxlen = max_review_length)

Features and labels

print(f"shape of X_train {X_train.shape}")
print(f"shape of y_train {y_train.shape}")
print(f"shape of X_test {X_test.shape}")
print(f"shape of y_test {y_test.shape}")

shape of X_train (25000, 500)
shape of y_train (25000,)
shape of X_test (25000, 500)
shape of y_test (25000,)

X_train[10]

array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
          0,    0,    0,    0,    0,    0,    1,  785,  189,  438,   47,
        110,  142,    7,    6, 7475,  120,    4,  236,  378,    7,  153,
         19,   87,  108,  141,   17, 1004,    5,    2,  883,    2,   23,
          8,    4,  136,    2,    2,    4, 7475,   43, 1076,   21, 1407,
        419,    5, 5202,  120,   91,  682,  189, 2818,    5,    9, 1348,
         31,    7,    4,  118,  785,  189,  108,  126,   93,    2,   16,
        540,  324,   23,    6,  364,  352,   21,   14,    9,   93,   56,
         18,   11,  230,   53,  771,   74,   31,   34,    4, 2834,    7,
          4,   22,    5,   14,   11,  471,    9,    2,   34,    4,  321,
        487,    5,  116,   15, 6584,    4,   22,    9,    6, 2286,    4,
        114, 2679,   23,  107,  293, 1008, 1172,    5,  328, 1236,    4,
       1375,  109,    9,    6,  132,  773,    2, 1412,    8, 1172,   18,
       7865,   29,    9,  276,   11,    6, 2768,   19,  289,  409,    4,
       5341, 2140,    2,  648, 1430,    2, 8914,    5,   27, 3000, 1432,
       7130,  103,    6,  346,  137,   11,    4, 2768,  295,   36, 7740,
        725,    6, 3208,  273,   11,    4, 1513,   15, 1367,   35,  154,
          2,  103,    2,  173,    7,   12,   36,  515, 3547,   94, 2547,
       1722,    5, 3547,   36,  203,   30,  502,    8,  361,   12,    8,
        989,  143,    4, 1172, 3404,   10,   10,  328, 1236,    9,    6,
         55,  221, 2989,    5,  146,  165,  179,  770,   15,   50,  713,
         53,  108,  448,   23,   12,   17,  225,   38,   76, 4397,   18,
        183,    8,   81,   19,   12,   45, 1257,    8,  135,   15,    2,
        166,    4,  118,    7,   45,    2,   17,  466,   45,    2,    4,
         22,  115,  165,  764, 6075,    5, 1030,    8, 2973,   73,  469,
        167, 2127,    2, 1568,    6,   87,  841,   18,    4,   22,    4,
        192,   15,   91,    7,   12,  304,  273, 1004,    4, 1375, 1172,
       2768,    2,   15,    4,   22,  764,   55, 5773,    5,   14, 4233,
       7444,    4, 1375,  326,    7,    4, 4760, 1786,    8,  361, 1236,
          8,  989,   46,    7,    4, 2768,   45,   55,  776,    8,   79,
        496,   98,   45,  400,  301,   15,    4, 1859,    9,    4,  155,
         15,   66,    2,   84,    5,   14,   22, 1534,   15,   17,    4,
        167,    2,   15,   75,   70,  115,   66,   30,  252,    7,  618,
         51,    9, 2161,    4, 3130,    5,   14, 1525,    8, 6584,   15,
          2,  165,  127, 1921,    8,   30,  179, 2532,    4,   22,    9,
        906,   18,    6,  176,    7, 1007, 1005,    4, 1375,  114,    4,
        105,   26,   32,   55,  221,   11,   68,  205,   96,    5,    4,
        192,   15,    4,  274,  410,  220,  304,   23,   94,  205,  109,
          9,   55,   73,  224,  259, 3786,   15,    4,   22,  528, 1645,
         34,    4,  130,  528,   30,  685,  345,   17,    4,  277,  199,
        166,  281,    5, 1030,    8,   30,  179, 4442,  444,    2,    9,
          6,  371,   87,  189,   22,    5,   31,    7,    4,  118,    7,
          4, 2068,  545, 1178,  829])

y_train[0]

1

Decoding

word_dict = imdb.get_word_index()
word_dict = { key:(value+3) for key, value in word_dict.items() }
word_dict[''] = 0                                             
word_dict['>'] = 1
word_dict['?'] = 2

a = []
for key, value in word_dict.items():
    a.append(value)

print(sorted(a))

[0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2250, 2251, 2252, 2253, 2254, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299, 2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047, 3048, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 3182, 3183, 3184, 3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195, 3196, 3197, 3198, 3199, 3200, 3201, 3202, 3203, 3204, 3205, 3206, 3207, 3208, 3209, 3210, 3211, 3212, 3213, 3214, 3215, 3216, 3217, 3218, 3219, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356, 3357, 3358, 3359, 3360, 3361, 3362, 3363, 3364, 3365, 3366, 3367, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3376, 3377, 3378, 3379, 3380, 3381, 3382, 3383, 3384, 3385, 3386, 3387, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3396, 3397, 3398, 3399, 3400, 3401, 3402, 3403, 3404, 3405, 3406, 3407, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3546, 3547, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3558, 3559, 3560, 3561, 3562, 3563, 3564, 3565, 3566, 3567, 3568, 3569, 3570, 3571, 3572, 3573, 3574, 3575, 3576, 3577, 3578, 3579, 3580, 3581, 3582, 3583, 3584, 3585, 3586, 3587, 3588, 3589, 3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3599, 3600, 3601, 3602, 3603, 3604, 3605, 3606, 3607, 3608, 3609, 3610, 3611, 3612, 3613, 3614, 3615, 3616, 3617, 3618, 3619, 3620, 3621, 3622, 3623, 3624, 3625, 3626, 3627, 3628, 3629, 3630, 3631, 3632, 3633, 3634, 3635, 3636, 3637, 3638, 3639, 3640, 3641, 3642, 3643, 3644, 3645, 3646, 3647, 3648, 3649, 3650, 3651, 3652, 3653, 3654, 3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 3668, 3669, 3670, 3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 3679, 3680, 3681, 3682, 3683, 3684, 3685, 3686, 3687, 3688, 3689, 3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699, 3700, 3701, 3702, 3703, 3704, 3705, 3706, 3707, 3708, 3709, 3710, 3711, 3712, 3713, 3714, 3715, 3716, 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742, 3743, 3744, 3745, 3746, 3747, 3748, 3749, 3750, 3751, 3752, 3753, 3754, 3755, 3756, 3757, 3758, 3759, 3760, 3761, 3762, 3763, 3764, 3765, 3766, 3767, 3768, 3769, 3770, 3771, 3772, 3773, 3774, 3775, 3776, 3777, 3778, 3779, 3780, 3781, 3782, 3783, 3784, 3785, 3786, 3787, 3788, 3789, 3790, 3791, 3792, 3793, 3794, 3795, 3796, 3797, 3798, 3799, 3800, 3801, 3802, 3803, 3804, 3805, 3806, 3807, 3808, 3809, 3810, 3811, 3812, 3813, 3814, 3815, 3816, 3817, 3818, 3819, 3820, 3821, 3822, 3823, 3824, 3825, 3826, 3827, 3828, 3829, 3830, 3831, 3832, 3833, 3834, 3835, 3836, 3837, 3838, 3839, 3840, 3841, 3842, 3843, 3844, 3845, 3846, 3847, 3848, 3849, 3850, 3851, 3852, 3853, 3854, 3855, 3856, 3857, 3858, 3859, 3860, 3861, 3862, 3863, 3864, 3865, 3866, 3867, 3868, 3869, 3870, 3871, 3872, 3873, 3874, 3875, 3876, 3877, 3878, 3879, 3880, 3881, 3882, 3883, 3884, 3885, 3886, 3887, 3888, 3889, 3890, 3891, 3892, 3893, 3894, 3895, 3896, 3897, 3898, 3899, 3900, 3901, 3902, 3903, 3904, 3905, 3906, 3907, 3908, 3909, 3910, 3911, 3912, 3913, 3914, 3915, 3916, 3917, 3918, 3919, 3920, 3921, 3922, 3923, 3924, 3925, 3926, 3927, 3928, 3929, 3930, 3931, 3932, 3933, 3934, 3935, 3936, 3937, 3938, 3939, 3940, 3941, 3942, 3943, 3944, 3945, 3946, 3947, 3948, 3949, 3950, 3951, 3952, 3953, 3954, 3955, 3956, 3957, 3958, 3959, 3960, 3961, 3962, 3963, 3964, 3965, 3966, 3967, 3968, 3969, 3970, 3971, 3972, 3973, 3974, 3975, 3976, 3977, 3978, 3979, 3980, 3981, 3982, 3983, 3984, 3985, 3986, 3987, 3988, 3989, 3990, 3991, 3992, 3993, 3994, 3995, 3996, 3997, 3998, 3999, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 4036, 4037, 4038, 4039, 4040, 4041, 4042, 4043, 4044, 4045, 4046, 4047, 4048, 4049, 4050, 4051, 4052, 4053, 4054, 4055, 4056, 4057, 4058, 4059, 4060, 4061, 4062, 4063, 4064, 4065, 4066, 4067, 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 4086, 4087, 4088, 4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 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40157, 40158, 40159, 40160, 40161, 40162, 40163, 40164, 40165, 40166, 40167, 40168, 40169, 40170, 40171, 40172, 40173, 40174, 40175, 40176, 40177, 40178, 40179, 40180, 40181, 40182, 40183, 40184, 40185, 40186, 40187, 40188, 40189, 40190, 40191, 40192, 40193, 40194, 40195, 40196, 40197, 40198, 40199, 40200, 40201, 40202, 40203, 40204, 40205, 40206, 40207, 40208, 40209, 40210, 40211, 40212, 40213, 40214, 40215, 40216, 40217, 40218, 40219, 40220, 40221, 40222, 40223, 40224, 40225, 40226, 40227, 40228, 40229, 40230, 40231, 40232, 40233, 40234, 40235, 40236, 40237, 40238, 40239, 40240, 40241, 40242, 40243, 40244, 40245, 40246, 40247, 40248, 40249, 40250, 40251, 40252, 40253, 40254, 40255, 40256, 40257, 40258, 40259, 40260, 40261, 40262, 40263, 40264, 40265, 40266, 40267, 40268, 40269, 40270, 40271, 40272, 40273, 40274, 40275, 40276, 40277, 40278, 40279, 40280, 40281, 40282, 40283, 40284, 40285, 40286, 40287, 40288, 40289, 40290, 40291, 40292, 40293, 40294, 40295, 40296, 40297, 40298, 40299, 40300, 40301, 40302, 40303, 40304, 40305, 40306, 40307, 40308, 40309, 40310, 40311, 40312, 40313, 40314, 40315, 40316, 40317, 40318, 40319, 40320, 40321, 40322, 40323, 40324, 40325, 40326, 40327, 40328, 40329, 40330, 40331, 40332, 40333, 40334, 40335, 40336, 40337, 40338, 40339, 40340, 40341, 40342, 40343, 40344, 40345, 40346, 40347, 40348, 40349, 40350, 40351, 40352, 40353, 40354, 40355, 40356, 40357, 40358, 40359, 40360, 40361, 40362, 40363, 40364, 40365, 40366, 40367, 40368, 40369, 40370, 40371, 40372, 40373, 40374, 40375, 40376, 40377, 40378, 40379, 40380, 40381, 40382, 40383, 40384, 40385, 40386, 40387, 40388, 40389, 40390, 40391, 40392, 40393, 40394, 40395, 40396, 40397, 40398, 40399, 40400, 40401, 40402, 40403, 40404, 40405, 40406, 40407, 40408, 40409, 40410, 40411, 40412, 40413, 40414, 40415, 40416, 40417, 40418, 40419, 40420, 40421, 40422, 40423, 40424, 40425, 40426, 40427, 40428, 40429, 40430, 40431, 40432, 40433, 40434, 40435, 40436, 40437, 40438, 40439, 40440, 40441, 40442, 40443, 40444, 40445, 40446, 40447, 40448, 40449, 40450, 40451, 40452, 40453, 40454, 40455, 40456, 40457, 40458, 40459, 40460, 40461, 40462, 40463, 40464, 40465, 40466, 40467, 40468, 40469, 40470, 40471, 40472, 40473, 40474, 40475, 40476, 40477, 40478, 40479, 40480, 40481, 40482, 40483, 40484, 40485, 40486, 40487, 40488, 40489, 40490, 40491, 40492, 40493, 40494, 40495, 40496, 40497, 40498, 40499, 40500, 40501, 40502, 40503, 40504, 40505, 40506, 40507, 40508, 40509, 40510, 40511, 40512, 40513, 40514, 40515, 40516, 40517, 40518, 40519, 40520, 40521, 40522, 40523, 40524, 40525, 40526, 40527, 40528, 40529, 40530, 40531, 40532, 40533, 40534, 40535, 40536, 40537, 40538, 40539, 40540, 40541, 40542, 40543, 40544, 40545, 40546, 40547, 40548, 40549, 40550, 40551, 40552, 40553, 40554, 40555, 40556, 40557, 40558, 40559, 40560, 40561, 40562, 40563, 40564, 40565, 40566, 40567, 40568, 40569, 40570, 40571, 40572, 40573, 40574, 40575, 40576, 40577, 40578, 40579, 40580, 40581, 40582, 40583, 40584, 40585, 40586, 40587, 40588, 40589, 40590, 40591, 40592, 40593, 40594, 40595, 40596, 40597, 40598, 40599, 40600, 40601, 40602, 40603, 40604, 40605, 40606, 40607, 40608, 40609, 40610, 40611, 40612, 40613, 40614, 40615, 40616, 40617, 40618, 40619, 40620, 40621, 40622, 40623, 40624, 40625, 40626, 40627, 40628, 40629, 40630, 40631, 40632, 40633, 40634, 40635, 40636, 40637, 40638, 40639, 40640, 40641, 40642, 40643, 40644, 40645, 40646, 40647, 40648, 40649, 40650, 40651, 40652, 40653, 40654, 40655, 40656, 40657, 40658, 40659, 40660, 40661, 40662, 40663, 40664, 40665, 40666, 40667, 40668, 40669, 40670, 40671, 40672, 40673, 40674, 40675, 40676, 40677, 40678, 40679, 40680, 40681, 40682, 40683, 40684, 40685, 40686, 40687, 40688, 40689, 40690, 40691, 40692, 40693, 40694, 40695, 40696, 40697, 40698, 40699, 40700, 40701, 40702, 40703, 40704, 40705, 40706, 40707, 40708, 40709, 40710, 40711, 40712, 40713, 40714, 40715, 40716, 40717, 40718, 40719, 40720, 40721, 40722, 40723, 40724, 40725, 40726, 40727, 40728, 40729, 40730, 40731, 40732, 40733, 40734, 40735, 40736, 40737, 40738, 40739, 40740, 40741, 40742, 40743, 40744, 40745, 40746, 40747, 40748, 40749, 40750, 40751, 40752, 40753, 40754, 40755, 40756, 40757, 40758, 40759, 40760, 40761, 40762, 40763, 40764, 40765, 40766, 40767, 40768, 40769, 40770, 40771, 40772, 40773, 40774, 40775, 40776, 40777, 40778, 40779, 40780, 40781, 40782, 40783, 40784, 40785, 40786, 40787, 40788, 40789, 40790, 40791, 40792, 40793, 40794, 40795, 40796, 40797, 40798, 40799, 40800, 40801, 40802, 40803, 40804, 40805, 40806, 40807, 40808, 40809, 40810, 40811, 40812, 40813, 40814, 40815, 40816, 40817, 40818, 40819, 40820, 40821, 40822, 40823, 40824, 40825, 40826, 40827, 40828, 40829, 40830, 40831, 40832, 40833, 40834, 40835, 40836, 40837, 40838, 40839, 40840, 40841, 40842, 40843, 40844, 40845, 40846, 40847, 40848, 40849, 40850, 40851, 40852, 40853, 40854, 40855, 40856, 40857, 40858, 40859, 40860, 40861, 40862, 40863, 40864, 40865, 40866, 40867, 40868, 40869, 40870, 40871, 40872, 40873, 40874, 40875, 40876, 40877, 40878, 40879, 40880, 40881, 40882, 40883, 40884, 40885, 40886, 40887, 40888, 40889, 40890, 40891, 40892, 40893, 40894, 40895, 40896, 40897, 40898, 40899, 40900, 40901, 40902, 40903, 40904, 40905, 40906, 40907, 40908, 40909, 40910, 40911, 40912, 40913, 40914, 40915, 40916, 40917, 40918, 40919, 40920, 40921, 40922, 40923, 40924, 40925, 40926, 40927, 40928, 40929, 40930, 40931, 40932, 40933, 40934, 40935, 40936, 40937, 40938, 40939, 40940, 40941, 40942, 40943, 40944, 40945, 40946, 40947, 40948, 40949, 40950, 40951, 40952, 40953, 40954, 40955, 40956, 40957, 40958, 40959, 40960, 40961, 40962, 40963, 40964, 40965, 40966, 40967, 40968, 40969, 40970, 40971, 40972, 40973, 40974, 40975, 40976, 40977, 40978, 40979, 40980, 40981, 40982, 40983, 40984, 40985, 40986, 40987, 40988, 40989, 40990, 40991, 40992, 40993, 40994, 40995, 40996, 40997, 40998, 40999, 41000, 41001, 41002, 41003, 41004, 41005, 41006, 41007, 41008, 41009, 41010, 41011, 41012, 41013, 41014, 41015, 41016, 41017, 41018, 41019, 41020, 41021, 41022, 41023, 41024, 41025, 41026, 41027, 41028, 41029, 41030, 41031, 41032, 41033, 41034, 41035, 41036, 41037, 41038, 41039, 41040, 41041, 41042, 41043, 41044, 41045, 41046, 41047, 41048, 41049, 41050, 41051, 41052, 41053, 41054, 41055, 41056, 41057, 41058, 41059, 41060, 41061, 41062, 41063, 41064, 41065, 41066, 41067, 41068, 41069, 41070, 41071, 41072, 41073, 41074, 41075, 41076, 41077, 41078, 41079, 41080, 41081, 41082, 41083, 41084, 41085, 41086, 41087, 41088, 41089, 41090, 41091, 41092, 41093, 41094, 41095, 41096, 41097, 41098, 41099, 41100, 41101, 41102, 41103, 41104, 41105, 41106, 41107, 41108, 41109, 41110, 41111, 41112, 41113, 41114, 41115, 41116, 41117, 41118, 41119, 41120, 41121, 41122, 41123, 41124, 41125, 41126, 41127, 41128, 41129, 41130, 41131, 41132, 41133, 41134, 41135, 41136, 41137, 41138, 41139, 41140, 41141, 41142, 41143, 41144, 41145, 41146, 41147, 41148, 41149, 41150, 41151, 41152, 41153, 41154, 41155, 41156, 41157, 41158, 41159, 41160, 41161, 41162, 41163, 41164, 41165, 41166, 41167, 41168, 41169, 41170, 41171, 41172, 41173, 41174, 41175, 41176, 41177, 41178, 41179, 41180, 41181, 41182, 41183, 41184, 41185, 41186, 41187, 41188, 41189, 41190, 41191, 41192, 41193, 41194, 41195, 41196, 41197, 41198, 41199, 41200, 41201, 41202, 41203, 41204, 41205, 41206, 41207, 41208, 41209, 41210, 41211, 41212, 41213, 41214, 41215, 41216, 41217, 41218, 41219, 41220, 41221, 41222, 41223, 41224, 41225, 41226, 41227, 41228, 41229, 41230, 41231, 41232, 41233, 41234, 41235, 41236, 41237, 41238, 41239, 41240, 41241, 41242, 41243, 41244, 41245, 41246, 41247, 41248, 41249, 41250, 41251, 41252, 41253, 41254, 41255, 41256, 41257, 41258, 41259, 41260, 41261, 41262, 41263, 41264, 41265, 41266, 41267, 41268, 41269, 41270, 41271, 41272, 41273, 41274, 41275, 41276, 41277, 41278, 41279, 41280, 41281, 41282, 41283, 41284, 41285, 41286, 41287, 41288, 41289, 41290, 41291, 41292, 41293, 41294, 41295, 41296, 41297, 41298, 41299, 41300, 41301, 41302, 41303, 41304, 41305, 41306, 41307, 41308, 41309, 41310, 41311, 41312, 41313, 41314, 41315, 41316, 41317, 41318, 41319, 41320, 41321, 41322, 41323, 41324, 41325, 41326, 41327, 41328, 41329, 41330, 41331, 41332, 41333, 41334, 41335, 41336, 41337, 41338, 41339, 41340, 41341, 41342, 41343, 41344, 41345, 41346, 41347, 41348, 41349, 41350, 41351, 41352, 41353, 41354, 41355, 41356, 41357, 41358, 41359, 41360, 41361, 41362, 41363, 41364, 41365, 41366, 41367, 41368, 41369, 41370, 41371, 41372, 41373, 41374, 41375, 41376, 41377, 41378, 41379, 41380, 41381, 41382, 41383, 41384, 41385, 41386, 41387, 41388, 41389, 41390, 41391, 41392, 41393, 41394, 41395, 41396, 41397, 41398, 41399, 41400, 41401, 41402, 41403, 41404, 41405, 41406, 41407, 41408, 41409, 41410, 41411, 41412, 41413, 41414, 41415, 41416, 41417, 41418, 41419, 41420, 41421, 41422, 41423, 41424, 41425, 41426, 41427, 41428, 41429, 41430, 41431, 41432, 41433, 41434, 41435, 41436, 41437, 41438, 41439, 41440, 41441, 41442, 41443, 41444, 41445, 41446, 41447, 41448, 41449, 41450, 41451, 41452, 41453, 41454, 41455, 41456, 41457, 41458, 41459, 41460, 41461, 41462, 41463, 41464, 41465, 41466, 41467, 41468, 41469, 41470, 41471, 41472, 41473, 41474, 41475, 41476, 41477, 41478, 41479, 41480, 41481, 41482, 41483, 41484, 41485, 41486, 41487, 41488, 41489, 41490, 41491, 41492, 41493, 41494, 41495, 41496, 41497, 41498, 41499, 41500, 41501, 41502, 41503, 41504, 41505, 41506, 41507, 41508, 41509, 41510, 41511, 41512, 41513, 41514, 41515, 41516, 41517, 41518, 41519, 41520, 41521, 41522, 41523, 41524, 41525, 41526, 41527, 41528, 41529, 41530, 41531, 41532, 41533, 41534, 41535, 41536, 41537, 41538, 41539, 41540, 41541, 41542, 41543, 41544, 41545, 41546, 41547, 41548, 41549, 41550, 41551, 41552, 41553, 41554, 41555, 41556, 41557, 41558, 41559, 41560, 41561, 41562, 41563, 41564, 41565, 41566, 41567, 41568, 41569, 41570, 41571, 41572, 41573, 41574, 41575, 41576, 41577, 41578, 41579, 41580, 41581, 41582, 41583, 41584, 41585, 41586, 41587, 41588, 41589, 41590, 41591, 41592, 41593, 41594, 41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604, 41605, 41606, 41607, 41608, 41609, 41610, 41611, 41612, 41613, 41614, 41615, 41616, 41617, 41618, 41619, 41620, 41621, 41622, 41623, 41624, 41625, 41626, 41627, 41628, 41629, 41630, 41631, 41632, 41633, 41634, 41635, 41636, 41637, 41638, 41639, 41640, 41641, 41642, 41643, 41644, 41645, 41646, 41647, 41648, 41649, 41650, 41651, 41652, 41653, 41654, 41655, 41656, 41657, 41658, 41659, 41660, 41661, 41662, 41663, 41664, 41665, 41666, 41667, 41668, 41669, 41670, 41671, 41672, 41673, 41674, 41675, 41676, 41677, 41678, 41679, 41680, 41681, 41682, 41683, 41684, 41685, 41686, 41687, 41688, 41689, 41690, 41691, 41692, 41693, 41694, 41695, 41696, 41697, 41698, 41699, 41700, 41701, 41702, 41703, 41704, 41705, 41706, 41707, 41708, 41709, 41710, 41711, 41712, 41713, 41714, 41715, 41716, 41717, 41718, 41719, 41720, 41721, 41722, 41723, 41724, 41725, 41726, 41727, 41728, 41729, 41730, 41731, 41732, 41733, 41734, 41735, 41736, 41737, 41738, 41739, 41740, 41741, 41742, 41743, 41744, 41745, 41746, 41747, 41748, 41749, 41750, 41751, 41752, 41753, 41754, 41755, 41756, 41757, 41758, 41759, 41760, 41761, 41762, 41763, 41764, 41765, 41766, 41767, 41768, 41769, 41770, 41771, 41772, 41773, 41774, 41775, 41776, 41777, 41778, 41779, 41780, 41781, 41782, 41783, 41784, 41785, 41786, 41787, 41788, 41789, 41790, 41791, 41792, 41793, 41794, 41795, 41796, 41797, 41798, 41799, 41800, 41801, 41802, 41803, 41804, 41805, 41806, 41807, 41808, 41809, 41810, 41811, 41812, 41813, 41814, 41815, 41816, 41817, 41818, 41819, 41820, 41821, 41822, 41823, 41824, 41825, 41826, 41827, 41828, 41829, 41830, 41831, 41832, 41833, 41834, 41835, 41836, 41837, 41838, 41839, 41840, 41841, 41842, 41843, 41844, 41845, 41846, 41847, 41848, 41849, 41850, 41851, 41852, 41853, 41854, 41855, 41856, 41857, 41858, 41859, 41860, 41861, 41862, 41863, 41864, 41865, 41866, 41867, 41868, 41869, 41870, 41871, 41872, 41873, 41874, 41875, 41876, 41877, 41878, 41879, 41880, 41881, 41882, 41883, 41884, 41885, 41886, 41887, 41888, 41889, 41890, 41891, 41892, 41893, 41894, 41895, 41896, 41897, 41898, 41899, 41900, 41901, 41902, 41903, 41904, 41905, 41906, 41907, 41908, 41909, 41910, 41911, 41912, 41913, 41914, 41915, 41916, 41917, 41918, 41919, 41920, 41921, 41922, 41923, 41924, 41925, 41926, 41927, 41928, 41929, 41930, 41931, 41932, 41933, 41934, 41935, 41936, 41937, 41938, 41939, 41940, 41941, 41942, 41943, 41944, 41945, 41946, 41947, 41948, 41949, 41950, 41951, 41952, 41953, 41954, 41955, 41956, 41957, 41958, 41959, 41960, 41961, 41962, 41963, 41964, 41965, 41966, 41967, 41968, 41969, 41970, 41971, 41972, 41973, 41974, 41975, 41976, 41977, 41978, 41979, 41980, 41981, 41982, 41983, 41984, 41985, 41986, 41987, 41988, 41989, 41990, 41991, 41992, 41993, 41994, 41995, 41996, 41997, 41998, 41999, 42000, 42001, 42002, 42003, 42004, 42005, 42006, 42007, 42008, 42009, 42010, 42011, 42012, 42013, 42014, 42015, 42016, 42017, 42018, 42019, 42020, 42021, 42022, 42023, 42024, 42025, 42026, 42027, 42028, 42029, 42030, 42031, 42032, 42033, 42034, 42035, 42036, 42037, 42038, 42039, 42040, 42041, 42042, 42043, 42044, 42045, 42046, 42047, 42048, 42049, 42050, 42051, 42052, 42053, 42054, 42055, 42056, 42057, 42058, 42059, 42060, 42061, 42062, 42063, 42064, 42065, 42066, 42067, 42068, 42069, 42070, 42071, 42072, 42073, 42074, 42075, 42076, 42077, 42078, 42079, 42080, 42081, 42082, 42083, 42084, 42085, 42086, 42087, 42088, 42089, 42090, 42091, 42092, 42093, 42094, 42095, 42096, 42097, 42098, 42099, 42100, 42101, 42102, 42103, 42104, 42105, 42106, 42107, 42108, 42109, 42110, 42111, 42112, 42113, 42114, 42115, 42116, 42117, 42118, 42119, 42120, 42121, 42122, 42123, 42124, 42125, 42126, 42127, 42128, 42129, 42130, 42131, 42132, 42133, 42134, 42135, 42136, 42137, 42138, 42139, 42140, 42141, 42142, 42143, 42144, 42145, 42146, 42147, 42148, 42149, 42150, 42151, 42152, 42153, 42154, 42155, 42156, 42157, 42158, 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type(word_dict)

dict

'is' in word_dict

True

reverse_word_dict = { value:key for key, value in word_dict.items() }

type(reverse_word_dict)

dict

len(word_dict)

88587

i = 9
print(' '.join(reverse_word_dict[id] for id in X_train[i]))
print("\nReview: ",'Positive' if y_train[i] == 1 else 'Negative')

                                                                                                                                                                                                                                                                                                                                                                                  > this movie has many problem associated with it that makes it come off like a low budget class project from someone in film school i have to give it credit on its ? though many times throughout the movie i found myself laughing hysterically it was so bad at times that it was comical which made it a fun watch br br if you're looking for a low grade slasher movie with a twist of psychological horror and a dash of campy ? then pop a bowl of popcorn invite some friends over and have some fun br br i agree with other comments that the sound is very bad dialog is next to impossible to follow much of the time and the soundtrack is kind of just there

Review:  Negative

y = pd.Series(y_train)

y.value_counts()

1    12500
0    12500
dtype: int64

Model building using NN

embedding_vector_length = 32
model = Sequential()
model.add(Embedding(10000, embedding_vector_length, input_length=max_review_length))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 500, 32)           320000    
                                                                 
 flatten (Flatten)           (None, 16000)             0         
                                                                 
 dense (Dense)               (None, 16)                256016    
                                                                 
 dense_1 (Dense)             (None, 16)                272       
                                                                 
 dense_2 (Dense)             (None, 1)                 17        
                                                                 
=================================================================
Total params: 576,305
Trainable params: 576,305
Non-trainable params: 0
_________________________________________________________________
None

call_backs = EarlyStopping(monitor='val_loss', verbose=1, mode='min', patience=100)
hist = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=128, callbacks=call_backs, verbose=0)

_, train_accuracy = model.evaluate(X_train, y_train, verbose=1)
_, test_accuracy = model.evaluate(X_test, y_test, verbose=1)
print('Train: %.3f, Test: %.3f' % (train_accuracy, test_accuracy))

782/782 [==============================] - 4s 5ms/step - loss: 4.0855e-09 - accuracy: 1.0000
782/782 [==============================] - 3s 4ms/step - loss: 1.2279 - accuracy: 0.8720
Train: 1.000, Test: 0.872

y_pred = model.predict(X_test)

782/782 [==============================] - 3s 4ms/step

Analysis

i = 4
print(' '.join(reverse_word_dict[id] for id in X_test[i]))
print("\nActual Review: ",'Positive' if y_test[i] == 1 else 'Negative')
print("\nPredicted Review: ",'Positive' if y_pred[i] == 1 else 'Negative')

                                                                                                                                                                                                                                                                                                                                                                                                        > like some other people wrote i'm a die hard mario fan and i loved this game br br this game starts slightly boring but trust me it's worth it as soon as you start your hooked the levels are fun and ? they will hook you ? your mind turns to ? i'm not kidding this game is also ? and is beautifully done br br to keep this spoiler free i have to keep my mouth shut about details but please try this game it'll be worth it br br story 9 9 action 10 1 it's that good ? 10 attention ? 10 average 10

Actual Review:  Positive

Predicted Review:  Positive

def review(text):
    text = text.lower()
    token = text.split()
    
    test = []
    for w in token:
        if w in word_dict:
            test.append(word_dict[w])
    
    test = pad_sequences([test], maxlen = max_review_length)
    result = model.predict(np.array([test][0]))[0][0]
    print("\nReview: ",'Positive' if result > 0.8 else 'Negative')

txt = input("Enter a review: ")
review(txt)

Enter a review: it is so boring to sit in this movie
1/1 [==============================] - 0s 40ms/step

Review:  Negative

txt = input("Enter a review: ")
review(txt)

Enter a review: worth watching
1/1 [==============================] - 0s 35ms/step

Review:  Positive

Social Media analytics

News-Headlines-Dataset-For-Sarcasm-Detection

pip install wordcloud

Collecting wordcloud
  Downloading wordcloud-1.8.2.2-cp39-cp39-win_amd64.whl (153 kB)
Requirement already satisfied: numpy>=1.6.1 in c:\users\arunv\anaconda3\lib\site-packages (from wordcloud) (1.20.3)
Requirement already satisfied: matplotlib in c:\users\arunv\anaconda3\lib\site-packages (from wordcloud) (3.4.3)
Requirement already satisfied: pillow in c:\users\arunv\anaconda3\lib\site-packages (from wordcloud) (8.4.0)
Requirement already satisfied: cycler>=0.10 in c:\users\arunv\anaconda3\lib\site-packages (from matplotlib->wordcloud) (0.10.0)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\arunv\anaconda3\lib\site-packages (from matplotlib->wordcloud) (2.8.2)
Requirement already satisfied: pyparsing>=2.2.1 in c:\users\arunv\anaconda3\lib\site-packages (from matplotlib->wordcloud) (3.0.4)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\arunv\anaconda3\lib\site-packages (from matplotlib->wordcloud) (1.3.1)
Requirement already satisfied: six in c:\users\arunv\anaconda3\lib\site-packages (from cycler>=0.10->matplotlib->wordcloud) (1.16.0)
Installing collected packages: wordcloud
Successfully installed wordcloud-1.8.2.2
Note: you may need to restart the kernel to use updated packages.

import numpy as np
import pandas as pd
import nltk
from sklearn.preprocessing import LabelBinarizer
from nltk.corpus import stopwords
import re,string
import seaborn as sns
from keras.preprocessing.text import Tokenizer
import tensorflow as tf
from keras.utils import pad_sequences
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense,Embedding,LSTM,Dropout,Bidirectional,GRU
import tensorflow as tf
from wordcloud import WordCloud

Reading the data

data = pd.read_json('Sarcasm_Headlines_Dataset.json', lines=True)
data.head(5)

	is_sarcastic	headline	article_link
0	1	thirtysomething scientists unveil doomsday clo...	https://www.theonion.com/thirtysomething-scien...
1	0	dem rep. totally nails why congress is falling...	https://www.huffingtonpost.com/entry/donna-edw...
2	0	eat your veggies: 9 deliciously different recipes	https://www.huffingtonpost.com/entry/eat-your-...
3	1	inclement weather prevents liar from getting t...	https://local.theonion.com/inclement-weather-p...
4	1	mother comes pretty close to using word 'strea...	https://www.theonion.com/mother-comes-pretty-c...

data = data.drop(['article_link'], axis = 1)

data.head(5)

	is_sarcastic	headline
0	1	thirtysomething scientists unveil doomsday clo...
1	0	dem rep. totally nails why congress is falling...
2	0	eat your veggies: 9 deliciously different recipes
3	1	inclement weather prevents liar from getting t...
4	1	mother comes pretty close to using word 'strea...

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 28619 entries, 0 to 28618
Data columns (total 2 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   is_sarcastic  28619 non-null  int64 
 1   headline      28619 non-null  object
dtypes: int64(1), object(1)
memory usage: 447.3+ KB

data.isna().sum()

is_sarcastic    0
headline        0
dtype: int64

data.describe(include = 'object')

	headline
count	28619
unique	28503
top	'no way to prevent this,' says only nation whe...
freq	12

data.shape

(28619, 2)

sns.countplot(x = 'is_sarcastic', data = data)

<AxesSubplot:xlabel='is_sarcastic', ylabel='count'>

import matplotlib.pyplot as plt
plt.figure(figsize = (20,20))
wc = WordCloud(max_words = 2000 , width = 1600 , height = 800).generate(" ".join(data[data.is_sarcastic == 0].headline))
plt.imshow(wc , interpolation = 'bilinear')

<matplotlib.image.AxesImage at 0x1a7c18b5610>

plt.figure(figsize = (20,20))
wc = WordCloud(max_words = 2000 , width = 1600 , height = 800).generate(" ".join(data[data.is_sarcastic == 1].headline))
plt.imshow(wc , interpolation = 'bilinear')

<matplotlib.image.AxesImage at 0x1a7c1e18a30>

data['Length'] = [len(i) for i in data.headline]

data['word_indices'] = [[(word.start(), word.end()-1) for word in re.finditer(r'\S+', line)] for line in data.headline]

data.head(5)

	is_sarcastic	headline	Length	word_indices
0	1	thirtysomething scientists unveil doomsday clo...	61	[(0, 14), (16, 25), (27, 32), (34, 41), (43, 4...
1	0	dem rep. totally nails why congress is falling...	79	[(0, 2), (4, 7), (9, 15), (17, 21), (23, 25), ...
2	0	eat your veggies: 9 deliciously different recipes	49	[(0, 2), (4, 7), (9, 16), (18, 18), (20, 30), ...
3	1	inclement weather prevents liar from getting t...	52	[(0, 8), (10, 16), (18, 25), (27, 30), (32, 35...
4	1	mother comes pretty close to using word 'strea...	61	[(0, 5), (7, 11), (13, 18), (20, 24), (26, 27)...

Data cleaning

stopwords = set(stopwords.words('english'))

data['headline']

0        thirtysomething scientists unveil doomsday clo...
1        dem rep. totally nails why congress is falling...
2        eat your veggies: 9 deliciously different recipes
3        inclement weather prevents liar from getting t...
4        mother comes pretty close to using word 'strea...
                               ...                        
28614         jews to celebrate rosh hashasha or something
28615    internal affairs investigator disappointed con...
28616    the most beautiful acceptance speech this week...
28617    mars probe destroyed by orbiting spielberg-gat...
28618                   dad clarifies this not a food stop
Name: headline, Length: 28619, dtype: object

pattern = '[^A-Za-z]+'
data.headline = data.headline.apply(lambda s : re.sub(pattern," ",s))

data.headline = data.headline.apply(lambda s: s.lower())

data.headline = data.headline.apply(lambda s: s.strip())

data.headline = data.headline.apply(lambda t: ' '.join([words for words in t.split() if words not in stopwords]))

words = []
for i in data.headline.values:
    words.append(i.split())
words[:5]

[['thirtysomething',
  'scientists',
  'unveil',
  'doomsday',
  'clock',
  'hair',
  'loss'],
 ['dem',
  'rep',
  'totally',
  'nails',
  'congress',
  'falling',
  'short',
  'gender',
  'racial',
  'equality'],
 ['eat', 'veggies', 'deliciously', 'different', 'recipes'],
 ['inclement', 'weather', 'prevents', 'liar', 'getting', 'work'],
 ['mother',
  'comes',
  'pretty',
  'close',
  'using',
  'word',
  'streaming',
  'correctly']]

Features = data['headline'] \ labels = data['is_sarchastic']

x_train,x_test,y_train,y_test = train_test_split(data.headline,data.is_sarcastic, test_size = 0.3 , random_state = 0)

max_features = 35000
maxlen = 200

tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(x_train)
tokenized_train = tokenizer.texts_to_sequences(x_train)
x_train = pad_sequences(tokenized_train, maxlen=maxlen)

tokenized_test = tokenizer.texts_to_sequences(x_test)
X_test = pad_sequences(tokenized_test, maxlen=maxlen)

word_index = tokenizer.word_index

len(word_index)

21999

#vocabulary
word_index

{'trump': 1,
 'new': 2,
 'man': 3,
 'report': 4,
 'one': 5,
 'year': 6,
 'woman': 7,
 'day': 8,
 'area': 9,
 'time': 10,
 'u': 11,
 'donald': 12,
 'says': 13,
 'first': 14,
 'obama': 15,
 'old': 16,
 'get': 17,
 'women': 18,
 'world': 19,
 'like': 20,
 'life': 21,
 'people': 22,
 'nation': 23,
 'house': 24,
 'make': 25,
 'white': 26,
 'back': 27,
 'clinton': 28,
 'still': 29,
 'family': 30,
 'could': 31,
 'way': 32,
 'americans': 33,
 'gop': 34,
 'study': 35,
 'would': 36,
 'black': 37,
 'american': 38,
 'years': 39,
 'bill': 40,
 'really': 41,
 'last': 42,
 'show': 43,
 'finds': 44,
 'know': 45,
 'home': 46,
 'president': 47,
 'state': 48,
 'watch': 49,
 'going': 50,
 'mom': 51,
 'school': 52,
 'best': 53,
 'video': 54,
 'death': 55,
 'good': 56,
 'every': 57,
 'police': 58,
 'things': 59,
 'america': 60,
 'big': 61,
 'child': 62,
 'hillary': 63,
 'campaign': 64,
 'right': 65,
 'high': 66,
 'party': 67,
 'parents': 68,
 'getting': 69,
 'love': 70,
 'say': 71,
 'work': 72,
 'take': 73,
 'may': 74,
 'health': 75,
 'kids': 76,
 'need': 77,
 'gets': 78,
 'john': 79,
 'dead': 80,
 'self': 81,
 'makes': 82,
 'little': 83,
 'go': 84,
 'court': 85,
 'baby': 86,
 'war': 87,
 'real': 88,
 'stop': 89,
 'never': 90,
 'change': 91,
 'calls': 92,
 'gay': 93,
 'look': 94,
 'takes': 95,
 'star': 96,
 'another': 97,
 'week': 98,
 'local': 99,
 'dad': 100,
 'election': 101,
 'two': 102,
 'even': 103,
 'news': 104,
 'game': 105,
 'want': 106,
 'around': 107,
 'plan': 108,
 'college': 109,
 'sex': 110,
 'bush': 111,
 'night': 112,
 'dog': 113,
 'live': 114,
 'next': 115,
 'guy': 116,
 'made': 117,
 'got': 118,
 'job': 119,
 'senate': 120,
 'god': 121,
 'debate': 122,
 'ever': 123,
 'children': 124,
 'office': 125,
 'thing': 126,
 'see': 127,
 'million': 128,
 'actually': 129,
 'congress': 130,
 'gun': 131,
 'son': 132,
 'long': 133,
 'reveals': 134,
 'us': 135,
 'away': 136,
 'national': 137,
 'finally': 138,
 'help': 139,
 'law': 140,
 'much': 141,
 'wants': 142,
 'without': 143,
 'couple': 144,
 'top': 145,
 'teen': 146,
 'friends': 147,
 'better': 148,
 'season': 149,
 'trying': 150,
 'announces': 151,
 'care': 152,
 'everyone': 153,
 'let': 154,
 'anti': 155,
 'shows': 156,
 'face': 157,
 'men': 158,
 'food': 159,
 'tell': 160,
 'facebook': 161,
 'money': 162,
 'already': 163,
 'friend': 164,
 'media': 165,
 'enough': 166,
 'give': 167,
 'business': 168,
 'bad': 169,
 'pope': 170,
 'making': 171,
 'york': 172,
 'paul': 173,
 'movie': 174,
 'mother': 175,
 'free': 176,
 'ways': 177,
 'part': 178,
 'sanders': 179,
 'students': 180,
 'introduces': 181,
 'shooting': 182,
 'sexual': 183,
 'city': 184,
 'think': 185,
 'former': 186,
 'entire': 187,
 'father': 188,
 'tv': 189,
 'story': 190,
 'company': 191,
 'government': 192,
 'attack': 193,
 'fight': 194,
 'find': 195,
 'pretty': 196,
 'body': 197,
 'supreme': 198,
 'must': 199,
 'climate': 200,
 'deal': 201,
 'end': 202,
 'history': 203,
 'photos': 204,
 'north': 205,
 'run': 206,
 'fire': 207,
 'second': 208,
 'book': 209,
 'keep': 210,
 'single': 211,
 'unveils': 212,
 'super': 213,
 'behind': 214,
 'student': 215,
 'something': 216,
 'releases': 217,
 'power': 218,
 'car': 219,
 'morning': 220,
 'forced': 221,
 'line': 222,
 'film': 223,
 'girl': 224,
 'wife': 225,
 'middle': 226,
 'great': 227,
 'security': 228,
 'scientists': 229,
 'th': 230,
 'republican': 231,
 'thinks': 232,
 'presidential': 233,
 'open': 234,
 'support': 235,
 'found': 236,
 'fans': 237,
 'human': 238,
 'public': 239,
 'republicans': 240,
 'wedding': 241,
 'looking': 242,
 'call': 243,
 'email': 244,
 'control': 245,
 'social': 246,
 'secret': 247,
 'speech': 248,
 'boy': 249,
 'admits': 250,
 'democrats': 251,
 'asks': 252,
 'used': 253,
 'coming': 254,
 'might': 255,
 'come': 256,
 'country': 257,
 'today': 258,
 'service': 259,
 'hot': 260,
 'race': 261,
 'full': 262,
 'times': 263,
 'post': 264,
 'killed': 265,
 'breaking': 266,
 'states': 267,
 'christmas': 268,
 'group': 269,
 'political': 270,
 'tax': 271,
 'name': 272,
 'michael': 273,
 'case': 274,
 'claims': 275,
 'girlfriend': 276,
 'ryan': 277,
 'goes': 278,
 'win': 279,
 'james': 280,
 'team': 281,
 'talk': 282,
 'employee': 283,
 'future': 284,
 'california': 285,
 'bernie': 286,
 'ceo': 287,
 'inside': 288,
 'fucking': 289,
 'many': 290,
 'minutes': 291,
 'obamacare': 292,
 'living': 293,
 'vote': 294,
 'use': 295,
 'days': 296,
 'sure': 297,
 'ad': 298,
 'department': 299,
 'candidate': 300,
 'town': 301,
 'ban': 302,
 'record': 303,
 'poll': 304,
 'looks': 305,
 'plans': 306,
 'month': 307,
 'running': 308,
 'voters': 309,
 'water': 310,
 'fan': 311,
 'rights': 312,
 'violence': 313,
 'music': 314,
 'lost': 315,
 'marriage': 316,
 'tells': 317,
 'always': 318,
 'head': 319,
 'wrong': 320,
 'mike': 321,
 'twitter': 322,
 'three': 323,
 'left': 324,
 'iran': 325,
 'room': 326,
 'probably': 327,
 'working': 328,
 'teacher': 329,
 'earth': 330,
 'administration': 331,
 'shit': 332,
 'hours': 333,
 'summer': 334,
 'daughter': 335,
 'cruz': 336,
 'idea': 337,
 'put': 338,
 'wall': 339,
 'russia': 340,
 'taking': 341,
 'together': 342,
 'believe': 343,
 'person': 344,
 'restaurant': 345,
 'art': 346,
 'king': 347,
 'warns': 348,
 'female': 349,
 'perfect': 350,
 'heart': 351,
 'reports': 352,
 'needs': 353,
 'judge': 354,
 'someone': 355,
 'biden': 356,
 'yet': 357,
 'past': 358,
 'hard': 359,
 'eating': 360,
 'giving': 361,
 'prison': 362,
 'save': 363,
 'gives': 364,
 'everything': 365,
 'lives': 366,
 'half': 367,
 'phone': 368,
 'air': 369,
 'shot': 370,
 'sleep': 371,
 'wearing': 372,
 'crisis': 373,
 'director': 374,
 'well': 375,
 'photo': 376,
 'list': 377,
 'girls': 378,
 'washington': 379,
 'online': 380,
 'age': 381,
 'kind': 382,
 'cancer': 383,
 'leave': 384,
 'beautiful': 385,
 'francis': 386,
 'ice': 387,
 'romney': 388,
 'dies': 389,
 'red': 390,
 'rock': 391,
 'owner': 392,
 'education': 393,
 'meet': 394,
 'attacks': 395,
 'texas': 396,
 'kill': 397,
 'ex': 398,
 'kim': 399,
 'young': 400,
 'place': 401,
 'ready': 402,
 'talks': 403,
 'street': 404,
 'officials': 405,
 'drug': 406,
 'china': 407,
 'thousands': 408,
 'buy': 409,
 'dream': 410,
 'huge': 411,
 'george': 412,
 'small': 413,
 'system': 414,
 'thought': 415,
 'mark': 416,
 'bar': 417,
 'secretary': 418,
 'store': 419,
 'kid': 420,
 'fbi': 421,
 'stars': 422,
 'cat': 423,
 'missing': 424,
 'moment': 425,
 'ted': 426,
 'pay': 427,
 'leaves': 428,
 'percent': 429,
 'different': 430,
 'tips': 431,
 'feel': 432,
 'military': 433,
 'word': 434,
 'latest': 435,
 'south': 436,
 'meeting': 437,
 'leaders': 438,
 'start': 439,
 'comes': 440,
 'community': 441,
 'nuclear': 442,
 'hour': 443,
 'fun': 444,
 'wins': 445,
 'problem': 446,
 'abortion': 447,
 'date': 448,
 'senator': 449,
 'personal': 450,
 'korea': 451,
 'internet': 452,
 'special': 453,
 'offers': 454,
 'set': 455,
 'interview': 456,
 'read': 457,
 'straight': 458,
 'david': 459,
 'waiting': 460,
 'watching': 461,
 'hit': 462,
 'tweets': 463,
 'class': 464,
 'guide': 465,
 'isis': 466,
 'birthday': 467,
 'prince': 468,
 'hollywood': 469,
 'pence': 470,
 'scott': 471,
 'ask': 472,
 'order': 473,
 'florida': 474,
 'play': 475,
 'series': 476,
 'trip': 477,
 'millions': 478,
 'congressman': 479,
 'cover': 480,
 'justice': 481,
 'break': 482,
 'third': 483,
 'bring': 484,
 'non': 485,
 'march': 486,
 'hands': 487,
 'federal': 488,
 'months': 489,
 'whole': 490,
 'move': 491,
 'reason': 492,
 'majority': 493,
 'letter': 494,
 'immigration': 495,
 'mass': 496,
 'k': 497,
 'assault': 498,
 'less': 499,
 'response': 500,
 'politics': 501,
 'happy': 502,
 'holiday': 503,
 'fall': 504,
 'travel': 505,
 'al': 506,
 'rules': 507,
 'chris': 508,
 'knows': 509,
 'protest': 510,
 'discover': 511,
 'chief': 512,
 'stephen': 513,
 'following': 514,
 'sports': 515,
 'issues': 516,
 'side': 517,
 'outside': 518,
 'murder': 519,
 'hair': 520,
 'favorite': 521,
 'across': 522,
 'struggling': 523,
 'front': 524,
 'fuck': 525,
 'lessons': 526,
 'c': 527,
 'hope': 528,
 'turns': 529,
 'jimmy': 530,
 'breaks': 531,
 'cut': 532,
 'birth': 533,
 'hate': 534,
 'muslim': 535,
 'cop': 536,
 'fashion': 537,
 'rise': 538,
 'vows': 539,
 'light': 540,
 'drunk': 541,
 'gift': 542,
 'investigation': 543,
 'nothing': 544,
 'reasons': 545,
 'questions': 546,
 'united': 547,
 'huffpost': 548,
 'late': 549,
 'minute': 550,
 'since': 551,
 'told': 552,
 'career': 553,
 'longer': 554,
 'talking': 555,
 'sign': 556,
 'football': 557,
 'anything': 558,
 'early': 559,
 'opens': 560,
 'words': 561,
 'song': 562,
 'called': 563,
 'fox': 564,
 'lot': 565,
 'apple': 566,
 'visit': 567,
 'feels': 568,
 'west': 569,
 'voice': 570,
 'victims': 571,
 'celebrates': 572,
 'russian': 573,
 'lose': 574,
 'leader': 575,
 'decision': 576,
 'try': 577,
 'experience': 578,
 'risk': 579,
 'point': 580,
 'message': 581,
 'jr': 582,
 'billion': 583,
 'evidence': 584,
 'spends': 585,
 'completely': 586,
 'turn': 587,
 'become': 588,
 'box': 589,
 'killing': 590,
 'reality': 591,
 'center': 592,
 'workers': 593,
 'union': 594,
 'trailer': 595,
 'grandma': 596,
 'number': 597,
 'dating': 598,
 'clearly': 599,
 'adds': 600,
 'puts': 601,
 'chinese': 602,
 'mueller': 603,
 'onion': 604,
 'employees': 605,
 'trans': 606,
 'teens': 607,
 'syria': 608,
 'excited': 609,
 'thinking': 610,
 'un': 611,
 'die': 612,
 'hoping': 613,
 'j': 614,
 'plane': 615,
 'brings': 616,
 'ferguson': 617,
 'hall': 618,
 'university': 619,
 'true': 620,
 'transgender': 621,
 'using': 622,
 'key': 623,
 'moving': 624,
 'users': 625,
 'dance': 626,
 'announce': 627,
 'apartment': 628,
 'taylor': 629,
 'important': 630,
 'racist': 631,
 'wars': 632,
 'democratic': 633,
 'coworker': 634,
 'michelle': 635,
 'iowa': 636,
 'mind': 637,
 'syrian': 638,
 'crime': 639,
 'data': 640,
 'sick': 641,
 'st': 642,
 'dinner': 643,
 'press': 644,
 'joe': 645,
 'road': 646,
 'candidates': 647,
 'robert': 648,
 'policy': 649,
 'learned': 650,
 'governor': 651,
 'worth': 652,
 'schools': 653,
 'experts': 654,
 'dying': 655,
 'lead': 656,
 'accidentally': 657,
 'rubio': 658,
 'worried': 659,
 'queer': 660,
 'bus': 661,
 'doctor': 662,
 'five': 663,
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 'flight': 996,
 'hotel': 997,
 'justin': 998,
 'budget': 999,
 'activists': 1000,
 ...}

Glove embeddings

embeddings_index = {}

f = open('glove.6B.200d.txt',encoding="utf8")

for line in f:
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype = 'float32')
    embeddings_index[word] = coefs
f.close()

embeddings_index

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        -0.28373  ,  0.75999  ,  0.58805  , -0.7334   ,  0.29339  ],
       dtype=float32),
 'attention': array([ 4.3522e-01,  3.6916e-01, -1.1480e-01, -2.1760e-01, -5.9948e-02,
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        -7.5472e-02,  8.5355e-02,  3.0579e-04, -8.6645e-02, -2.5820e-02],
       dtype=float32),
 ...}

all_embs = np.stack(embeddings_index.values())

C:\Users\arunv\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3364: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.
  if (await self.run_code(code, result,  async_=asy)):

all_embs.shape

(400000, 200)

nb_words = min(max_features, len(word_index))

nb_words

21999

emb_mean,emb_std = all_embs.mean(), all_embs.std()
embed_size = all_embs.shape[1]

embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))
for word, i in word_index.items():
    if i >= max_features: 
        continue
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None: 
        embedding_matrix[i] = embedding_vector

embedding_matrix.shape

(21999, 200)

Building bidirectional LSTM sequential NN model

model = Sequential()
model.add(Embedding(nb_words, output_dim=embed_size, weights=[embedding_matrix], input_length=200, trainable=True))
model.add(Bidirectional(LSTM(units=128 , recurrent_dropout = 0.5 , dropout = 0.5)))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer=keras.optimizers.Adam(learning_rate = 0.01), loss='binary_crossentropy', metrics=['acc'])

model.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding_1 (Embedding)     (None, 200, 200)          4399800   
                                                                 
 bidirectional_1 (Bidirectio  (None, 256)              336896    
 nal)                                                            
                                                                 
 dense_1 (Dense)             (None, 1)                 257       
                                                                 
=================================================================
Total params: 4,736,953
Trainable params: 4,736,953
Non-trainable params: 0
_________________________________________________________________

history = model.fit(x_train, y_train, batch_size = 30 , validation_data = (X_test,y_test) , epochs = 1)

105/668 [===>..........................] - ETA: 23:10 - loss: 0.3921 - acc: 0.8279

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_2528/3300003160.py in <module>
----> 1 history = model.fit(x_train, y_train, batch_size = 30 , validation_data = (X_test,y_test) , epochs = 1)

~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
     62     filtered_tb = None
     63     try:
---> 64       return fn(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)

~\anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1407                 _r=1):
   1408               callbacks.on_train_batch_begin(step)
-> 1409               tmp_logs = self.train_function(iterator)
   1410               if data_handler.should_sync:
   1411                 context.async_wait()

~\anaconda3\lib\site-packages\tensorflow\python\util\traceback_utils.py in error_handler(*args, **kwargs)
    148     filtered_tb = None
    149     try:
--> 150       return fn(*args, **kwargs)
    151     except Exception as e:
    152       filtered_tb = _process_traceback_frames(e.__traceback__)

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    913 
    914       with OptionalXlaContext(self._jit_compile):
--> 915         result = self._call(*args, **kwds)
    916 
    917       new_tracing_count = self.experimental_get_tracing_count()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    945       # In this case we have created variables on the first call, so we run the
    946       # defunned version which is guaranteed to never create variables.
--> 947       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    948     elif self._stateful_fn is not None:
    949       # Release the lock early so that multiple threads can perform the call

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
   2451       (graph_function,
   2452        filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 2453     return graph_function._call_flat(
   2454         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
   2455 

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1858         and executing_eagerly):
   1859       # No tape is watching; skip to running the function.
-> 1860       return self._build_call_outputs(self._inference_function.call(
   1861           ctx, args, cancellation_manager=cancellation_manager))
   1862     forward_backward = self._select_forward_and_backward_functions(

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    495       with _InterpolateFunctionError(self):
    496         if cancellation_manager is None:
--> 497           outputs = execute.execute(
    498               str(self.signature.name),
    499               num_outputs=self._num_outputs,

~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52   try:
     53     ctx.ensure_initialized()
---> 54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:

KeyboardInterrupt: 

print("Accuracy: " , model.evaluate(X_test,y_test)[1]*100)

269/269 [==============================] - 100s 371ms/step - loss: 0.4700 - acc: 0.7886
Accuracy:  78.86093854904175

pred = np.round(model.predict(X_test))
pred[:5]

269/269 [==============================] - 81s 300ms/step

array([[1.],
       [0.],
       [1.],
       [0.],
       [1.]], dtype=float32)

y_test[:5]

151      1
17228    0
4725     1
4258     0
12250    1
Name: is_sarcastic, dtype: int64

Classification report

from sklearn.metrics import classification_report,confusion_matrix,accuracy_score

print(classification_report(y_test, pred, target_names = ['Not Sarcastic','Sarcastic']))

               precision    recall  f1-score   support

Not Sarcastic       0.80      0.81      0.80      4560
    Sarcastic       0.78      0.77      0.77      4026

     accuracy                           0.79      8586
    macro avg       0.79      0.79      0.79      8586
 weighted avg       0.79      0.79      0.79      8586

cm = confusion_matrix(y_test,pred)
cm

array([[3689,  871],
       [ 944, 3082]], dtype=int64)

cm = pd.DataFrame(cm , index = ['Not Sarcastic','Sarcastic'] , columns = ['Not Sarcastic','Sarcastic'])
plt.figure(figsize = (10,10))
sns.heatmap(cm,cmap= "Blues", linecolor = 'black' , linewidth = 1 , annot = True, fmt='' , xticklabels = ['Not Sarcastic','Sarcastic'] , yticklabels = ['Not Sarcastic','Sarcastic'])

<AxesSubplot:>